ANN-to-SNN Conversion

Convert trained PyTorch ANNs to rate-coded spiking neural networks.

Contract

The conversion package is an optional PyTorch surface. The base package can be imported without PyTorch; resolving convert, ConvertedSNN, or QCFSActivation requires a PyTorch-capable environment.

• convert(model, calibration_data=None, T=None, percentile=99.9) extracts Linear and Conv2d weights and selects a conversion route from the model's activations, returning a deterministic ConvertedSNN:
  • Threshold-balancing route (ReLU models, Diehl et al. 2015) — calibrates activation thresholds from ReLU layers when calibration data is supplied, starts each IF neuron from rest, and defaults T to 16.
  • QCFS route (QCFS-trained models, Bu et al. 2022) — takes each QCFSActivation's learned threshold directly, pre-loads each IF neuron to theta / 2 (the optimal shift that cancels the quantisation bias), ignores calibration data, and adopts the layers' trained step budget when T is left unset.
• ConvertedSNN.run(x) rate-codes NumPy input with a fixed RNG seed and returns output spike counts for one vector or a batch. initial_membrane_fraction controls the per-layer membrane pre-load (0.0 rest, 0.5 QCFS shift).
• ConvertedSNN.classify(x) returns the argmax class index from output spike counts.
• QCFSActivation replaces ReLU during conversion-aware training by clipping activations to [0, theta] and quantising them to T + 1 spike-rate levels with a straight-through gradient.
• replace_relu_with_qcfs(model, T=8, theta=1.0, learn_theta=True) substitutes every ReLU/ReLU6 in a model (recursing through submodules) with a QCFSActivation, preparing the network for QCFS conversion-aware fine-tuning.

Verification

The public conversion files are covered by the scoped NumPy-docstring policy:

• src/sc_neurocore/conversion/__init__.py
• src/sc_neurocore/conversion/ann_to_snn.py
• src/sc_neurocore/conversion/qcfs.py

Focused production tests live in tests/test_conversion.py and tests/test_conversion_ann_snn.py. They exercise real PyTorch modules, the threshold-balancing and QCFS conversion routes, ConvertedSNN.run, ConvertedSNN.classify, the membrane shift, the ReLU→QCFS substitution helper, QCFS range and gradient behaviour, and the layer-extraction contract.

Converter

::: sc_neurocore.conversion.ann_to_snn options: show_root_heading: true members: - convert - ConvertedSNN - replace_relu_with_qcfs

QCFS Activation

::: sc_neurocore.conversion.qcfs options: show_root_heading: true members: - QCFSActivation